Near-infrared spectroscopy as a tool for monitoring the spatial variability of sugarcane quality in the fields

Corrêdo, Lucas P.; Wei, Marcelo Chan Fu; Ferraz, Marcos Nascimbem; Molin, José Paulo

doi:10.1016/j.biosystemseng.2021.04.001

Cited by 13 publications

(5 citation statements)

References 39 publications

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“…Outra tecnologia muito utilizada é o NIRS (espectroscopia no infravermelho próximo). Esta inovação pode alterar a forma como os produtores tomam decisões na área, sendo uma ferramenta que possibilita obter dados da qualidade da matéria prima de forma rápida, acessível, com alta precisão e preços mais econômicos, além de ser sustentável ao meio ambiente (CORRÊDO, 2021).…”

Section: Evidências Do Impacto Econômico Da Agricultura De Precisão N...unclassified

Agricultura De Precisão E Novas Tecnologias: Impactos E Desafios No Setor Sucroenergético

Angélica Domingues de Carvalho,

CAROLINA FIDELIS FONSECA

2023

Anais Do 61º Congresso Da Sociedade Brasileira De Economia, Administração E Sociologia Rural (SOBER)

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Grupo de Trabalho (GT): 08 -Conhecimentos, tecnologias e inovações no ruralResumo O objetivo deste trabalho foi evidenciar como a agricultura de precisão tem sido absorvida e difundida no setor sucroenergético, compreender os seus impactos econômico, social e ambiental e identificar as dificuldades para sua implantação. A metodologia foi pautada em revisão sistemática de literatura, pesquisa teórico-exploratória e dados secundários. Os resultados indicaram que a agricultura de precisão trouxe impactos econômicos positivos como a redução de custos, precisão na tomada de decisão e melhor gerenciamento da propriedade, uniformidade no manejo e menor utilização de insumos. Os impactos sociais foram sentidos nos baixos níveis de instrução, contudo, os produtores investem pouco em capacitação para seus funcionários e a tecnologia acaba não sendo utilizada de forma adequada. Também foi constado a diminuição dos impactos negativos ao meio ambiente: menor contaminação ambiental, redução no aquecimento global, diminuição da compactação do solo e menor esgotamento fóssil. As dificuldades principais para a implantação da agricultura de precisão foram os altos custos da tecnologia e dos serviços terceirizados, além da falta de mão-de-obra capacitada para operá-las.

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Section: Evidências Do Impacto Econômico Da Agricultura De Precisão N...unclassified

Agricultura De Precisão E Novas Tecnologias: Impactos E Desafios No Setor Sucroenergético

Angélica Domingues de Carvalho,

CAROLINA FIDELIS FONSECA

2023

Anais Do 61º Congresso Da Sociedade Brasileira De Economia, Administração E Sociologia Rural (SOBER)

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“…Once calibrated, the NIR spectra can be used for the fast determination of the properties of the analyzed sample [18]. Thus, this technique is being used in very diverse industrial fields, such as food, biomedicine, pharmacy, petrochemicals, polymers, and the textile industry, among others [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Post-Consumer Textile Waste Classification through Near-Infrared Spectroscopy, Using an Advanced Deep Learning Approach

et al. 2022

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The textile industry is generating great environmental concerns due to the exponential growth of textile products’ consumption (fast fashion) and production. The textile value chain today operates as a linear system (textile products are produced, used, and discarded), thus putting pressure on resources and creating negative environmental impacts. A new textile economy based on the principles of circular economy is needed for a more sustainable textile industry. To help meet this challenge, an efficient collection, classification, and recycling system needs to be implemented at the end-of-life stage of textile products, so as to obtain high-quality recycled materials able to be reused in high-value products. This paper contributes to the classification of post-consumer textile waste by proposing an automatic classification method able to be trained to separate higher-quality textile fiber flows. Our proposal is the use of near-infrared (NIR) spectroscopy combined with a mathematical treatment of the spectra by convolutional neural networks (CNNs) to classify and separate 100% pure samples and binary mixtures of the most common textile fibers. CNN is applied for the first time to the classification of textile samples. A total of 370 textile samples were studied—50% used for calibration and 50% for prediction purposes. The results obtained are very promising (100% correct classification for pure fibers and 90–100% for binary mixtures), showing that the proposed methodology is very powerful, able to be trained for the specific separation of flows, and compatible with the automation of the system at an industrial scale.

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“…They are fast, non-destructive, cost-effective and environmentally friendly methods. NIR spectroscopy has been successfully applied as an analytical technique in different areas, such as the food [ 17 , 18 ], agriculture [ 19 , 20 ], fuel [ 21 ] and paper industries [ 22 ], among others, due to several advantages, including fast response, being reagent-free and noninvasiveness. MIR has been applied to identify cellulose-based fibers [ 23 ], natural rubber samples [ 24 ], changes in engine-oil properties [ 25 ] and to determine the thermal stability of explosives [ 26 ], among many other applications.…”

Section: Introductionmentioning

confidence: 99%

Classification of Textile Samples Using Data Fusion Combining Near- and Mid-Infrared Spectral Information

2022

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There is an urgent need to reuse and recycle textile fibers, since today, low recycling rates are achieved. Accurate classification methods for post-consumer textile waste are needed in the short term for a higher circularity in the textile and fashion industries. This paper compares different spectroscopic data from textile samples in order to correctly classify the textile samples. The accurate classification of textile waste results in higher recycling rates and a better quality of the recycled materials. The data fusion of near- and mid-infrared spectra is compared with single-spectrum information. The classification results show that data fusion is a better option, providing more accurate classification results, especially for difficult classification problems where the classes are wide and close to one another. The experimental results presented in this paper prove that the data fusion of near- and mid-infrared spectra is a good option for accurate textile-waste classification, since this approach allows the classification results to be significantly improved.

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Near-infrared spectroscopy as a tool for monitoring the spatial variability of sugarcane quality in the fields

Cited by 13 publications

References 39 publications

Agricultura De Precisão E Novas Tecnologias: Impactos E Desafios No Setor Sucroenergético

Agricultura De Precisão E Novas Tecnologias: Impactos E Desafios No Setor Sucroenergético

Post-Consumer Textile Waste Classification through Near-Infrared Spectroscopy, Using an Advanced Deep Learning Approach

Classification of Textile Samples Using Data Fusion Combining Near- and Mid-Infrared Spectral Information

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